A new formulation for web crippling strength of cold-formed steel sheeting using genetic programming

This study presents Genetic programming (GP) as a new tool for the formulation of web crippling strength of cold-formed steel decks for various loading cases. There is no well established analytical solution of the problem due to complex plastic behaviour. The objective of this study is to provide an alternative robust formulation to related design codes and to verify the robustness of GP for the formulation of such structural engineering problems. The training and testing patterns of the proposed GP formulation are based on well established experimental results from the literature. The GP based formulation results are compared with experimental results and current design codes and found to be more accurate.     

KP-Rank: a semantic-based unsupervised approach for keyphrase extraction from text data

Automatic key concept identification from text is the main challenging task in information extraction, information retrieval, digital libraries, ontology learning, and text analysis. The main difficulty lies in the issues with the text data itself, such as noise in text, diversity, scale of data, context dependency and word sense ambiguity. To cope with this challenge, numerous supervised and unsupervised approaches have been devised. The existing topical clustering-based approaches for keyphrase extraction are domain dependent and overlooks semantic similarity between candidate features while extracting the topical phrases. In this paper, a semantic based unsupervised approach (KP-Rank) is proposed for keyphrase extraction. In the proposed approach, we exploited Latent Semantic Analysis (LSA) and clustering techniques and a novel frequency-based algorithm for candidate ranking is introduced which considers locality-based sentence, paragraph and section frequencies. To evaluate the performance of the proposed method, three benchmark datasets (i.e. Inspec, 500N-KPCrowed and SemEval-2010) from different domains are used. The experimental results show that overall, the KP-Rank achieved significant improvements over the existing approaches on the selected performance measures.

     

Type 2 Diabetes Risk Prediction Using Deep Convolutional Neural Network Based-Bayesian Optimization

Diabetes mellitus is a long-term condition characterized by hyperglycemia. It could lead to plenty of difficulties. According to rising morbidity in recent years, the world’s diabetic patients will exceed 642 million by 2040, implying that one out of every ten persons will be diabetic. There is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’ lives. Due to its rapid development, deep learning (DL) was used to predict numerous diseases. However, DL methods still suffer from their limited prediction performance due to the hyperparameters selection and parameters optimization. Therefore, the selection of hyper-parameters is critical in improving classification performance. This study presents Convolutional Neural Network (CNN) that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm (BOA) has been employed for hyperparameters selection and parameters optimization. Two issues have been investigated and solved during the experiment to enhance the results. The first is the dataset class imbalance, which is solved using Synthetic Minority Oversampling Technique (SMOTE) technique. The second issue is the model's poor performance, which has been solved using the Bayesian optimization algorithm. The findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%, F1-score of 0.88.6, and Matthews Correlation Coefficient (MCC) of 0.88.6.     

Wavelet domain association rules for efficient texture classification

The wavelet domain association rules method is proposed for efficient texture characterization. The concept of association rules to capture the frequently occurring local intensity variation in textures. The frequency of occurrence of these local patterns within a region is used as texture features. Since texture is basically a multi-scale phenomenon, multi-resolution approaches such as wavelets, are expected to perform efficiently for texture analysis. Thus, this study proposes a new algorithm which uses the wavelet domain association rules for texture classification. Essentially, this work is an extension version of an early work of the Rushing et al. [10], [11], where the generation of intensity domain association rules generation was proposed for efficient texture characterization. The wavelet domain and the intensity domain (gray scale) association rules were generated for performance comparison purposes. As a result, Rushing et al. [10], [11] demonstrated that intensity domain association rules performs much more accurate results than those of the methods which were compared in the Rushing et al. work. Moreover, the performed experimental studies showed the effectiveness of the wavelet domain association rules than the intensity domain association rules for texture classification problem. The overall success rate is about 97%.     

Genetic programming-based attenuation relationship: An application of recent earthquakes in turkey

This study investigates an application of genetic programming (GP) for the prediction of peak ground acceleration (PGA) using strong-ground-motion data from Turkey. The input variables in the developed GP model are the average shear-wave velocity, earthquake source to site distance and earthquake magnitude, and the output is the PGA values. The proposed GP model is based on the most reliable database compiled for earthquakes in Turkey. The results show that the consistency between the observed PGA values and the predicted ones by the GP model yields relatively high correlation coefficients (R²=0.75). The proposed model is also compared with an existing attenuation relationship and found to be more accurate.     

Comparative performance analysis of solar powered supercritical-transcritical CO2 based systems for hydrogen production and multigeneration

CO₂ based power and refrigeration cycles have been developed and analyzed in different existing studies. However, the development of a CO₂ based comprehensive energy system and its performance analysis have not been considered. In this study, the integration of a CO₂ based solar parabolic trough collector system, a supercritical CO₂ power cycle, a transcritical CO₂ power cycle, and a CO₂ based cascade refrigeration system for hydrogen production and multigeneration purpose is analyzed thermodynamically. This study aims to analyze and compare the difference in the thermodynamic performance of comprehensive energy systems when CO₂ is used as the working fluid in all the cycles with a system that uses other working fluids. Therefore, two comprehensive energy systems with the same number of subsystems are designed to justify the comparison. The second comprehensive energy system uses liquid potassium instead of CO₂ as a working fluid in the solar parabolic trough collector and a steam cycle is used to replace the transcritical CO₂ power cycle. Results of the energy and exergy performance analysis of two comprehensive energy systems showed that the two systems can be used for the multigeneration purpose. However, the use of a steam cycle and potassium-based solar parabolic trough collector increases the comprehensive energy systems’ overall energy and exergy efficiency by 41.9% and 26.7% respectively. Also, the use of liquid potassium as working fluid in the parabolic trough collectors increases the absorbed solar energy input by 419 kW and 2100 kW thereby resulting in a 23% and 90.7% increase in energetic and exergetic efficiency respectively. The carbon emission reduction potential of the two comprehensive energy systems modelled in this study is also analyzed.     

Extraction optimisation,antioxidant activity and inhibition on α-amylase and pancreatic lipase of polyphenols from the seeds of Syzygium cumini

The objectives of this study were to determine the optimal extraction conditions of polyphenols from Syzygium cumini seeds by response surface methodology and investigate their antioxidant activity and inhibition on α-amylase and pancreatic lipase. As results, the optimal extraction conditions in the ultrasonic extraction process which maximised total polyphenols content, minimised the IC₅₀ values of α-amylase and pancreatic lipase were determined as follows: extraction time 60 min, ethanol concentration 63% and solvent/solid ratio 44 mL g⁻¹. The main phenolic compounds in partially purified fraction of Syzygium cumini seeds were catechin, epicatechin, kaempferol, gallic, 5-caffeoylquinic, caffeic and ferulic acids. In addition, the partially purified fraction inhibited 87.66 ± 5.55 and 86.61 ± 3.15% of α-amylase and pancreatic lipase, respectively. The results suggested that Syzygium cumini seeds could be explored as a natural antioxidant and could be used as a source of highly antidiabetic and anti-obesity bioactive compounds.     

Evaluation of greenhouse residues gasification performance in hydrogen production

In this study, a modeling study was carried out to investigate the potential of hydrogen production from greenhouse tomato and pepper residues blending in different rates (0%, 25%, 50%, 75% and 100%) by air-steam gasification. The numerical model developed for the gasification system assumes that all carbon in the mixture is gasified. Air to fuel rate and steam to fuel rates are 0.05 due to high content of O₂ in biomass residues. The gasifier temperature is 877 °C (1150 K) for developed model. According to the result of this study, increasing tomato residues blending rate increases hydrogen content of syngas. It is mainly caused by the content of O₂ in tomato residues being higher than content of O₂ in pepper residues. This study shows that the O₂ content of greenhouse residues is an important factor in syngas production, especially in H₂ production.     

A Novel Approach for Bolted T-Stub Connections

This paper reports an investigation into new connection types and their behaviors determined using full-scale experiments. T-shaped connections were created using the IPE standard profile. The aim of this study was to analyze the influence of T connections based on the IPE standard profile, height of beam to height of T-stub joint (H) of T-stub joints, and lengths (X) of T-stub joints on the behavior of steel connections, in order to provide the necessary data for improving Eurocode 3 and enable efficient use of residue IPE standard profiles and back to the consumption cycle. While the moment resistance values increased with an increase in H from H_min to H_max in model groups with X of 126 mm, and the energy dissipation increased with an increase in H from H_min to H_max and also with an increase in the lengths (X) of T-stub joints from 54 to 126 mm.